To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
* Data Mesh Architecture:
* Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
* Dataplex Lakes and Zones:
* Lakesin Dataplex are logical containers for managing data at scale, andzonesare subdivisions within lakes for organizing data based on domains, teams, or other criteria.
* Domain and Team Management:
* By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
* This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
* Create Lakes and Zones:
* Create separate lakes in Dataplex for each team (analytics and data science).
* Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
* Attach BigQuery Datasets:
* Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
* Decentralized Management:
* Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference Links:
* Dataplex Documentation
* BigQuery Documentation
* Data Mesh Principles